{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"E:\\pythonstudy\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#竞赛的评价指标为logloss\n",
    "from sklearn.metrics import log_loss  \n",
    "\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('diabetes.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#随机选取20%作为测序集\n",
    "from sklearn.cross_validation import train_test_split\n",
    "train, test = train_test_split(data, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06c9de6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#单个特征散点图 - BloodPressure\n",
    "plt.scatter(range(train.shape[0]),train[\"BloodPressure\"].values,color='green')\n",
    "plt.title(\"BloodPressure\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZi8esm4+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06ea73630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#单个特征散点图 - Glucose\n",
    "plt.scatter(range(train.shape[0]),train[\"Glucose\"].values,color='green')\n",
    "plt.title(\"Glucose\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06ebad5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#单个特征散点图 - SkinThickness\n",
    "plt.scatter(range(train.shape[0]),train[\"SkinThickness\"].values,color='green')\n",
    "plt.title(\"SkinThickness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/j/LAV5Vo8LSSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06ec3aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#单个特征散点图-BMI\n",
    "plt.scatter(range(train.shape[0]),train[\"BMI\"].values,color='green')\n",
    "plt.title(\"BMI\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#以上几个特征有一些为0，但是从实际的数据含义来看，这些特征不可能为0，故在训练集和测序集中均删除，\n",
    "#而怀孕次数为0是有实际意义的，Insulin为0的值太多，所以为避免0值对结果的影响，均加上0.00001\n",
    "\n",
    "train=train[train[\"BloodPressure\"]>0]\n",
    "train=train[train[\"Glucose\"]>0]\n",
    "train=train[train[\"BMI\"]>0]\n",
    "train=train[train[\"BloodPressure\"]>0]\n",
    "train[\"Pregnancies\"]+=1\n",
    "train[\"Insulin\"]+=0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#以上几个特征有一些为0，但是从实际的数据含义来看，这些特征不可能为0，故在训练集和测序集中均删除，\n",
    "#而怀孕次数为0是有实际意义的，Insulin为0的值太多，所以为避免0值对结果的影响，均加上0.00001\n",
    "test=test[test[\"BloodPressure\"]>0]\n",
    "test=test[test[\"Glucose\"]>0]\n",
    "test=test[test[\"BMI\"]>0]\n",
    "test=test[test[\"BloodPressure\"]>0]\n",
    "test[\"Pregnancies\"]+=1\n",
    "test[\"Insulin\"]+=0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train = train['Outcome'].values  #将目标值y取出来，其他均为x\n",
    "X_train = train.drop('Outcome',axis =1)\n",
    "# 以上为训练集\n",
    "y_test = test['Outcome'].values  \n",
    "X_test = test.drop('Outcome',axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.50492778 0.53388285 0.52228056 0.46422328 0.38724583]\n",
      "cv logloss is: 0.482512059480604\n"
     ]
    }
   ],
   "source": [
    "#调用逻辑回归模型\n",
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss)\n",
    "print ('cv logloss is:', -loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#调优参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00100255, 0.00060172, 0.00040107, 0.00080709, 0.00060906,\n",
       "        0.00099597, 0.00131407, 0.00079379, 0.00099788, 0.00059009,\n",
       "        0.0010025 , 0.00060177, 0.00040121, 0.00060668]),\n",
       " 'mean_score_time': array([0.00080223, 0.00060172, 0.00060196, 0.00020041, 0.00079145,\n",
       "        0.00120511, 0.00109453, 0.00081692, 0.00080724, 0.00080423,\n",
       "        0.00020051, 0.00040112, 0.00060134, 0.00039668]),\n",
       " 'mean_test_score': array([-0.69314718, -0.6440618 , -0.68585688, -0.52992526, -0.48396244,\n",
       "        -0.48243692, -0.48235238, -0.4826766 , -0.48327629, -0.48332535,\n",
       "        -0.48339502, -0.48340153, -0.48340705, -0.48340927]),\n",
       " 'mean_train_score': array([-0.69314718, -0.64223918, -0.68389888, -0.52245347, -0.47215528,\n",
       "        -0.46532699, -0.46099339, -0.46083975, -0.4607618 , -0.46076019,\n",
       "        -0.46075934, -0.46075933, -0.46075932, -0.46075932]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11, 10,  2,  1,  3,  4,  5,  6,  7,  8,  9]),\n",
       " 'split0_test_score': array([-0.69314718, -0.6450171 , -0.68181716, -0.53916019, -0.50191413,\n",
       "        -0.50256289, -0.50456956, -0.50492778, -0.50572223, -0.50577765,\n",
       "        -0.505866  , -0.50587352, -0.50588415, -0.50588323]),\n",
       " 'split0_train_score': array([-0.69314718, -0.64143331, -0.68004167, -0.51770276, -0.46504067,\n",
       "        -0.45849997, -0.45408823, -0.45390085, -0.45382159, -0.45381967,\n",
       "        -0.45381881, -0.45381879, -0.45381878, -0.45381878]),\n",
       " 'split1_test_score': array([-0.69314718, -0.64637006, -0.68490211, -0.54473403, -0.5159102 ,\n",
       "        -0.52177839, -0.53374122, -0.53388285, -0.5363494 , -0.53638961,\n",
       "        -0.53665736, -0.53666289, -0.53668823, -0.53669047]),\n",
       " 'split1_train_score': array([-0.69314718, -0.64050676, -0.6847498 , -0.51493661, -0.45806765,\n",
       "        -0.45214956, -0.44697429, -0.44682199, -0.44672503, -0.44672323,\n",
       "        -0.44672217, -0.44672215, -0.44672214, -0.44672214]),\n",
       " 'split2_test_score': array([-0.69314718, -0.65230126, -0.67825944, -0.56018549, -0.53154517,\n",
       "        -0.52296305, -0.52306315, -0.52228056, -0.5226307 , -0.5225634 ,\n",
       "        -0.52260223, -0.52259779, -0.52260091, -0.52260129]),\n",
       " 'split2_train_score': array([-0.69314718, -0.63792123, -0.67007401, -0.51300048, -0.46150066,\n",
       "        -0.45647203, -0.45246404, -0.45236726, -0.45229883, -0.45229785,\n",
       "        -0.45229712, -0.45229711, -0.4522971 , -0.4522971 ]),\n",
       " 'split3_test_score': array([-0.69314718, -0.64233261, -0.69122133, -0.51922558, -0.45757418,\n",
       "        -0.46378653, -0.46214659, -0.46422328, -0.46483677, -0.46506179,\n",
       "        -0.46513403, -0.46516037, -0.46516219, -0.46517038]),\n",
       " 'split3_train_score': array([-0.69314718, -0.64351017, -0.69148175, -0.52741262, -0.47987243,\n",
       "        -0.47080387, -0.46630491, -0.46613437, -0.46605129, -0.46604956,\n",
       "        -0.46604864, -0.46604863, -0.46604862, -0.46604861]),\n",
       " 'split4_test_score': array([-0.69314718, -0.63420296, -0.69314718, -0.48594184, -0.41225033,\n",
       "        -0.4003864 , -0.38742302, -0.38724583, -0.38600381, -0.38599522,\n",
       "        -0.38587479, -0.38587229, -0.38585885, -0.38586002]),\n",
       " 'split4_train_score': array([-0.69314718, -0.64782441, -0.69314718, -0.53921486, -0.49629497,\n",
       "        -0.4887095 , -0.4851355 , -0.48497426, -0.48491228, -0.48491064,\n",
       "        -0.48490998, -0.48490995, -0.48490995, -0.48490995]),\n",
       " 'std_fit_time': array([5.00111031e-07, 4.91303385e-04, 4.91205656e-04, 7.51840496e-04,\n",
       "        4.97729300e-04, 6.34996035e-04, 4.00395156e-04, 7.38736458e-04,\n",
       "        2.62341053e-05, 4.81977744e-04, 1.46506228e-06, 4.91341925e-04,\n",
       "        4.91380851e-04, 4.95416175e-04]),\n",
       " 'std_score_time': array([0.00040112, 0.00080237, 0.0004915 , 0.00040083, 0.00073699,\n",
       "        0.0004    , 0.00102247, 0.00040877, 0.00040374, 0.0004025 ,\n",
       "        0.00040102, 0.00049126, 0.00049099, 0.00048589]),\n",
       " 'std_test_score': array([0.        , 0.00589539, 0.00559803, 0.02552736, 0.04340026,\n",
       "        0.04612071, 0.05321347, 0.0530641 , 0.05406464, 0.05405513,\n",
       "        0.054159  , 0.05415919, 0.0541702 , 0.05416965]),\n",
       " 'std_train_score': array([9.93013661e-17, 3.31980508e-03, 8.36373391e-03, 9.73621027e-03,\n",
       "        1.41765923e-02, 1.32317863e-02, 1.36236573e-02, 1.36157016e-02,\n",
       "        1.36238291e-02, 1.36238011e-02, 1.36238918e-02, 1.36238904e-02,\n",
       "        1.36238911e-02, 1.36238912e-02])}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the complete results (list of named tuples)\n",
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.48235238018516874\n",
      "{'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEKCAYAAAA1qaOTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a06eee59b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame(grid.cv_results_).to_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#cvresult = pd.DataFrame.from_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#test_means = cv_results['mean_test_score']\n",
    "#test_stds = cv_results['std_test_score'] \n",
    "#train_means = cvresult['mean_train_score']\n",
    "#train_stds = cvresult['std_train_score'] \n",
    "\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'neg-logloss' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
